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体验新版 GitCode,发现更多精彩内容 >>
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85e63593
编写于
11月 11, 2019
作者:
C
channings
提交者:
GitHub
11月 11, 2019
浏览文件
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差异文件
Merge pull request #6 from PaddlePaddle/develop
merge master
上级
4118254f
873b1f6b
变更
8
隐藏空白更改
内联
并排
Showing
8 changed file
with
96 addition
and
185 deletion
+96
-185
README.md
README.md
+2
-1
op_list.md
op_list.md
+1
-1
x2paddle/__init__.py
x2paddle/__init__.py
+1
-1
x2paddle/convert.py
x2paddle/convert.py
+27
-12
x2paddle/core/op_mapper.py
x2paddle/core/op_mapper.py
+15
-8
x2paddle/decoder/caffe_decoder.py
x2paddle/decoder/caffe_decoder.py
+11
-1
x2paddle/op_mapper/tf_op_mapper.py
x2paddle/op_mapper/tf_op_mapper.py
+30
-81
x2paddle/op_mapper/tf_op_mapper_nhwc.py
x2paddle/op_mapper/tf_op_mapper_nhwc.py
+9
-80
未找到文件。
README.md
浏览文件 @
85e63593
...
...
@@ -10,7 +10,7 @@ X2Paddle在多个主流的CV模型上,测试过TensorFlow/Caffe/ONNX模型的
## 环境依赖
python == 2.7 | python >= 3.5
paddlepaddle >= 1.
5
.0
paddlepaddle >= 1.
6
.0
**按需安装以下依赖**
tensorflow : tensorflow == 1.14.0
...
...
@@ -55,6 +55,7 @@ x2paddle --framework=onnx --model=onnx_model.onnx --save_dir=pd_model
|--caffe_proto |
**[可选]**
由caffe.proto编译成caffe_pb2.py文件的存放路径,当存在自定义Layer时使用,默认为None |
|--without_data_format_optimization |
**[可选]**
For TensorFlow, 当指定该参数时,关闭NHWC->NCHW的优化,见
[
文档Q2
](
FAQ.md
)
|
|--define_input_shape |
**[可选]**
For TensorFlow, 当指定该参数时,强制用户输入每个Placeholder的shape,见
[
文档Q2
](
FAQ.md
)
|
|--params_merge |
**[可选]**
当指定该参数时,转换完成后,inference_model中的所有模型参数将合并保存为一个文件__params__ |
## 使用转换后的模型
...
...
op_list.md
浏览文件 @
85e63593
...
...
@@ -18,7 +18,7 @@
| 33 | Mean | 34 | MatMul | 35 | ArgMax | 36 | StridedSlice |
| 37 | Slice | 38 | Sum | 39 | Max | 40 | Conv2DBackpropInput |
| 41 | Cast | 42 | Split | 43 | Squeeze | 44 | ResizeNearestNeighbor |
| 45 | Softmax | 46 | Range | 47 | ConcatV2 |
| 45 | Softmax | 46 | Range | 47 | ConcatV2 |
48 | MirrorPad |
## Caffe
...
...
x2paddle/__init__.py
浏览文件 @
85e63593
__version__
=
"0.
5
.0"
__version__
=
"0.
6
.0"
x2paddle/convert.py
浏览文件 @
85e63593
...
...
@@ -68,6 +68,11 @@ def arg_parser():
action
=
"store_true"
,
default
=
False
,
help
=
"define input shape for tf model"
)
parser
.
add_argument
(
"--params_merge"
,
"-pm"
,
action
=
"store_true"
,
default
=
False
,
help
=
"define whether merge the params"
)
return
parser
...
...
@@ -75,7 +80,8 @@ def arg_parser():
def
tf2paddle
(
model_path
,
save_dir
,
without_data_format_optimization
=
False
,
define_input_shape
=
False
):
define_input_shape
=
False
,
params_merge
=
False
):
# check tensorflow installation and version
try
:
import
os
...
...
@@ -121,10 +127,10 @@ def tf2paddle(model_path,
optimizer
.
merge_bias
()
optimizer
.
make_nchw_input_output
()
optimizer
.
remove_transpose
()
mapper
.
save_inference_model
(
save_dir
)
mapper
.
save_inference_model
(
save_dir
,
params_merge
)
def
caffe2paddle
(
proto
,
weight
,
save_dir
,
caffe_proto
):
def
caffe2paddle
(
proto
,
weight
,
save_dir
,
caffe_proto
,
params_merge
=
False
):
from
x2paddle.decoder.caffe_decoder
import
CaffeDecoder
from
x2paddle.op_mapper.caffe_op_mapper
import
CaffeOpMapper
from
x2paddle.optimizer.caffe_optimizer
import
CaffeOptimizer
...
...
@@ -141,10 +147,10 @@ def caffe2paddle(proto, weight, save_dir, caffe_proto):
optimizer
=
CaffeOptimizer
(
mapper
)
optimizer
.
merge_bn_scale
()
optimizer
.
merge_op_activation
()
mapper
.
save_inference_model
(
save_dir
)
mapper
.
save_inference_model
(
save_dir
,
params_merge
)
def
onnx2paddle
(
model_path
,
save_dir
):
def
onnx2paddle
(
model_path
,
save_dir
,
params_merge
=
False
):
# check onnx installation and version
try
:
import
onnx
...
...
@@ -167,7 +173,7 @@ def onnx2paddle(model_path, save_dir):
optimizer
=
ONNXOptimizer
(
mapper
)
optimizer
.
delete_redundance_code
()
mapper
.
save_inference_model
(
save_dir
)
mapper
.
save_inference_model
(
save_dir
,
params_merge
)
def
main
():
...
...
@@ -182,7 +188,7 @@ def main():
if
args
.
version
:
import
x2paddle
print
(
"x2paddle-{} with python>=3.5, paddlepaddle>=1.
5
.0
\n
"
.
format
(
print
(
"x2paddle-{} with python>=3.5, paddlepaddle>=1.
6
.0
\n
"
.
format
(
x2paddle
.
__version__
))
return
...
...
@@ -192,8 +198,8 @@ def main():
try
:
import
paddle
v0
,
v1
,
v2
=
paddle
.
__version__
.
split
(
'.'
)
if
int
(
v0
)
!=
1
or
int
(
v1
)
<
5
:
print
(
"paddlepaddle>=1.
5
.0 is required"
)
if
int
(
v0
)
!=
1
or
int
(
v1
)
<
6
:
print
(
"paddlepaddle>=1.
6
.0 is required"
)
return
except
:
print
(
"paddlepaddle not installed, use
\"
pip install paddlepaddle
\"
"
)
...
...
@@ -202,20 +208,29 @@ def main():
assert
args
.
model
is
not
None
,
"--model should be defined while translating tensorflow model"
without_data_format_optimization
=
False
define_input_shape
=
False
params_merge
=
False
if
args
.
without_data_format_optimization
:
without_data_format_optimization
=
True
if
args
.
define_input_shape
:
define_input_shape
=
True
if
args
.
params_merge
:
params_merge
=
True
tf2paddle
(
args
.
model
,
args
.
save_dir
,
without_data_format_optimization
,
define_input_shape
)
define_input_shape
,
params_merge
)
elif
args
.
framework
==
"caffe"
:
assert
args
.
prototxt
is
not
None
and
args
.
weight
is
not
None
,
"--prototxt and --weight should be defined while translating caffe model"
params_merge
=
False
if
args
.
params_merge
:
params_merge
=
True
caffe2paddle
(
args
.
prototxt
,
args
.
weight
,
args
.
save_dir
,
args
.
caffe_proto
)
args
.
caffe_proto
,
params_merge
)
elif
args
.
framework
==
"onnx"
:
assert
args
.
model
is
not
None
,
"--model should be defined while translating onnx model"
onnx2paddle
(
args
.
model
,
args
.
save_dir
)
params_merge
=
False
if
args
.
params_merge
:
params_merge
=
True
onnx2paddle
(
args
.
model
,
args
.
save_dir
,
params_merge
)
else
:
raise
Exception
(
"--framework only support tensorflow/caffe/onnx now"
)
...
...
x2paddle/core/op_mapper.py
浏览文件 @
85e63593
...
...
@@ -110,7 +110,7 @@ class OpMapper(object):
self
.
add_codes
(
"import paddle.fluid as fluid"
)
self
.
add_codes
(
""
)
def
save_inference_model
(
self
,
save_dir
):
def
save_inference_model
(
self
,
save_dir
,
params_merge
):
self
.
save_python_model
(
save_dir
)
import
sys
...
...
@@ -138,13 +138,20 @@ class OpMapper(object):
py_code_dir
,
fluid
.
default_main_program
(),
predicate
=
if_exist
)
fluid
.
io
.
save_inference_model
(
dirname
=
os
.
path
.
join
(
save_dir
,
"inference_model"
),
feeded_var_names
=
input_names
,
target_vars
=
outputs
,
executor
=
exe
,
params_filename
=
None
)
if
params_merge
:
fluid
.
io
.
save_inference_model
(
dirname
=
os
.
path
.
join
(
save_dir
,
"inference_model"
),
feeded_var_names
=
input_names
,
target_vars
=
outputs
,
executor
=
exe
,
params_filename
=
"__params__"
)
else
:
fluid
.
io
.
save_inference_model
(
dirname
=
os
.
path
.
join
(
save_dir
,
"inference_model"
),
feeded_var_names
=
input_names
,
target_vars
=
outputs
,
executor
=
exe
,
params_filename
=
None
)
except
:
raise
Exception
(
"Paddle code was saved in {}/model.py, but seems there's wrong exist, please check model.py manually."
...
...
x2paddle/decoder/caffe_decoder.py
浏览文件 @
85e63593
...
...
@@ -243,7 +243,17 @@ class CaffeDecoder(object):
for
blob
in
layer
.
blobs
:
if
len
(
blob
.
shape
.
dim
):
dims
=
blob
.
shape
.
dim
c_o
,
c_i
,
h
,
w
=
map
(
int
,
[
1
]
*
(
4
-
len
(
dims
))
+
list
(
dims
))
if
layer
.
type
==
'PReLU'
:
c_o
,
c_i
,
h
,
w
=
map
(
int
,
[
1
]
+
\
list
(
dims
)
+
[
1
]
*
(
3
-
len
(
dims
)))
elif
layer
.
type
==
'Normalize'
:
data
=
np
.
asarray
(
list
(
blob
.
data
),
dtype
=
np
.
float32
)
transformed
.
append
(
data
)
continue
else
:
c_o
,
c_i
,
h
,
w
=
map
(
int
,
[
1
]
*
(
4
-
len
(
dims
))
\
+
list
(
dims
))
else
:
c_o
=
blob
.
num
c_i
=
blob
.
channels
...
...
x2paddle/op_mapper/tf_op_mapper.py
浏览文件 @
85e63593
...
...
@@ -278,6 +278,7 @@ class TFOpMapper(OpMapper):
'name'
:
string
(
node
.
layer_name
),
'append_batch_size'
:
False
}
if
shape
[
0
]
<
0
:
self
.
batch_node
=
node
...
...
@@ -382,7 +383,6 @@ class TFOpMapper(OpMapper):
data_format
=
node
.
get_attr
(
"data_format"
).
decode
()
pad_mode
=
node
.
get_attr
(
"padding"
).
decode
()
channel_first
=
data_format
==
"NCHW"
padding
=
0
if
not
channel_first
:
in_shape
=
[
in_shape
[
i
]
for
i
in
[
0
,
3
,
1
,
2
]]
...
...
@@ -391,22 +391,10 @@ class TFOpMapper(OpMapper):
else
:
self
.
graph
.
data_format_propagation
(
node
)
if
pad_mode
==
"SAME"
:
pad_h
=
get_same_padding
(
in_shape
[
2
],
k_size
[
2
],
strides
[
2
])
pad_w
=
get_same_padding
(
in_shape
[
3
],
k_size
[
3
],
strides
[
3
])
pad_h
=
pad_h
[
0
]
+
pad_h
[
1
]
pad_w
=
pad_w
[
0
]
+
pad_w
[
1
]
if
pad_h
!=
0
or
pad_w
!=
0
:
attr
=
{
"paddings"
:
[
0
,
pad_h
,
0
,
pad_w
],
"pad_value"
:
-
10000.0
}
node
.
fluid_code
.
add_layer
(
"pad2d"
,
inputs
=
input
,
output
=
node
,
param_attr
=
attr
)
input
=
node
attr
=
{
"pool_size"
:
k_size
[
2
:
4
],
"pool_type"
:
string
(
"max"
),
"pool_padding"
:
padding
,
"pool_padding"
:
string
(
pad_mode
)
,
"pool_stride"
:
strides
[
2
:
4
]
}
node
.
fluid_code
.
add_layer
(
"pool2d"
,
...
...
@@ -432,7 +420,6 @@ class TFOpMapper(OpMapper):
data_format
=
node
.
get_attr
(
"data_format"
).
decode
()
pad_mode
=
node
.
get_attr
(
"padding"
).
decode
()
channel_first
=
data_format
==
"NCHW"
padding
=
0
self
.
weights
[
kernel
.
layer_name
.
replace
(
'/'
,
'_'
)]
=
numpy
.
transpose
(
kernel
.
value
,
(
3
,
2
,
0
,
1
))
...
...
@@ -444,18 +431,6 @@ class TFOpMapper(OpMapper):
else
:
self
.
graph
.
data_format_propagation
(
node
)
if
pad_mode
==
"SAME"
:
pad_h
=
get_same_padding
(
in_shape
[
2
],
k_size
[
0
],
strides
[
2
])
pad_w
=
get_same_padding
(
in_shape
[
3
],
k_size
[
1
],
strides
[
3
])
if
pad_h
[
0
]
==
pad_h
[
1
]
and
pad_w
[
0
]
==
pad_w
[
1
]:
padding
=
[
pad_h
[
0
],
pad_w
[
0
]]
else
:
attr
=
{
"paddings"
:
pad_h
+
pad_w
,
"pad_value"
:
0.0
}
node
.
fluid_code
.
add_layer
(
"pad2d"
,
inputs
=
input
,
output
=
node
,
param_attr
=
attr
)
input
=
node
attr
=
{
"bias_attr"
:
False
,
"param_attr"
:
string
(
kernel
.
layer_name
),
...
...
@@ -463,7 +438,7 @@ class TFOpMapper(OpMapper):
"filter_size"
:
k_size
[
0
:
2
],
"stride"
:
strides
[
2
:
4
],
"dilation"
:
dilations
[
2
:
4
],
"padding"
:
padding
"padding"
:
string
(
pad_mode
)
}
node
.
fluid_code
.
add_layer
(
"conv2d"
,
inputs
=
input
,
...
...
@@ -535,7 +510,6 @@ class TFOpMapper(OpMapper):
data_format
=
node
.
get_attr
(
"data_format"
).
decode
()
pad_mode
=
node
.
get_attr
(
"padding"
).
decode
()
channel_first
=
data_format
==
"NCHW"
padding
=
0
self
.
weights
[
kernel
.
layer_name
.
replace
(
'/'
,
'_'
)]
=
numpy
.
transpose
(
kernel
.
value
,
(
2
,
3
,
0
,
1
))
...
...
@@ -547,19 +521,6 @@ class TFOpMapper(OpMapper):
else
:
self
.
data_format_propagation
(
node
)
if
pad_mode
==
"SAME"
:
pad_h
=
get_same_padding
(
in_shape
[
2
],
k_size
[
0
],
strides
[
2
])
pad_w
=
get_same_padding
(
in_shape
[
3
],
k_size
[
1
],
strides
[
3
])
if
pad_h
[
0
]
==
pad_h
[
1
]
and
pad_w
[
0
]
==
pad_w
[
1
]:
padding
=
[
pad_h
[
0
],
pad_w
[
0
]]
else
:
attr
=
{
"paddings"
:
pad_h
+
pad_w
,
"pad_value"
:
0.0
}
node
.
fluid_code
.
add_layer
(
"pad2d"
,
inputs
=
input
,
output
=
node
,
param_attr
=
attr
)
input
=
node
attr
=
{
"bias_attr"
:
False
,
"param_attr"
:
string
(
kernel
.
layer_name
),
...
...
@@ -569,7 +530,7 @@ class TFOpMapper(OpMapper):
"dilation"
:
dilations
[
2
:
4
],
"groups"
:
k_size
[
3
]
*
in_shape
[
1
],
"use_cudnn"
:
False
,
"padding"
:
padding
"padding"
:
string
(
pad_mode
)
}
node
.
fluid_code
.
add_layer
(
"conv2d"
,
inputs
=
input
,
...
...
@@ -691,14 +652,9 @@ class TFOpMapper(OpMapper):
attr
=
{
"pool_size"
:
k_size
[
2
:
4
],
"pool_type"
:
string
(
"avg"
),
"pool_stride"
:
strides
[
2
:
4
]
"pool_stride"
:
strides
[
2
:
4
],
"pool_padding"
:
string
(
pad_mode
)
}
if
pad_mode
==
"SAME"
:
pad_h
=
get_same_padding
(
in_shape
[
2
],
k_size
[
2
],
strides
[
2
])
pad_w
=
get_same_padding
(
in_shape
[
3
],
k_size
[
3
],
strides
[
3
])
assert
pad_h
[
0
]
==
pad_h
[
1
]
and
pad_w
[
0
]
==
pad_w
[
1
],
"Cannot map AvgPool"
attr
[
"pool_padding"
]
=
[
pad_h
[
0
],
pad_w
[
0
]]
node
.
fluid_code
.
add_layer
(
"pool2d"
,
inputs
=
input
,
output
=
node
,
...
...
@@ -806,6 +762,28 @@ class TFOpMapper(OpMapper):
output
=
node
,
param_attr
=
attr
)
def
MirrorPad
(
self
,
node
):
input
=
self
.
graph
.
get_node
(
node
.
layer
.
input
[
0
],
copy
=
True
)
paddings
=
self
.
graph
.
get_node
(
node
.
layer
.
input
[
1
],
copy
=
True
)
assert
paddings
.
layer_type
==
"Const"
,
"Padding should be Const"
self
.
add_omit_nodes
(
paddings
.
layer_name
,
node
.
layer_name
)
paddings
=
paddings
.
value
.
flatten
().
tolist
()
mode
=
node
.
get_attr
(
"mode"
).
decode
()
assert
mode
==
"REFLECT"
,
"Only support 'REFLECT` mode in MirrorPad"
if
input
.
tf_data_format
==
"NHWC"
and
len
(
input
.
out_shapes
[
0
])
==
4
:
paddings
=
[
paddings
[
i
]
for
i
in
[
0
,
1
,
6
,
7
,
2
,
3
,
4
,
5
]]
pad_op
=
"pad"
if
len
(
input
.
out_shapes
[
0
])
==
4
:
if
paddings
[
0
]
+
paddings
[
1
]
+
paddings
[
2
]
+
paddings
[
3
]
==
0
:
paddings
=
paddings
[
4
:]
pad_op
=
"pad2d"
attr
=
{
"paddings"
:
paddings
,
"mode"
:
string
(
"reflect"
)}
node
.
fluid_code
.
add_layer
(
pad_op
,
inputs
=
input
,
output
=
node
,
param_attr
=
attr
)
def
Range
(
self
,
node
):
start
=
self
.
graph
.
get_node
(
node
.
layer
.
input
[
0
],
copy
=
True
)
limit
=
self
.
graph
.
get_node
(
node
.
layer
.
input
[
1
],
copy
=
True
)
...
...
@@ -993,20 +971,6 @@ class TFOpMapper(OpMapper):
else
:
self
.
data_format_propagation
(
node
)
padding
=
0
if
pad_mode
==
"SAME"
:
pad_h
=
get_same_padding
(
in_shape
[
2
],
k_size
[
0
],
strides
[
2
])
pad_w
=
get_same_padding
(
in_shape
[
3
],
k_size
[
1
],
strides
[
3
])
if
pad_h
[
0
]
==
pad_h
[
1
]
and
pad_w
[
0
]
==
pad_w
[
1
]:
padding
=
[
pad_h
[
0
],
pad_w
[
0
]]
else
:
attr
=
{
"paddings"
:
pad_h
+
pad_w
,
"pad_value"
:
0.0
}
node
.
fluid_code
.
add_layer
(
"pad2d"
,
inputs
=
input
,
output
=
node
,
param_attr
=
attr
)
input
=
node
attr
=
{
"bias_attr"
:
False
,
"param_attr"
:
string
(
kernel
.
layer_name
),
...
...
@@ -1014,29 +978,14 @@ class TFOpMapper(OpMapper):
"filter_size"
:
k_size
[
0
:
2
],
"stride"
:
strides
[
2
:
4
],
"dilation"
:
dilations
[
2
:
4
],
"padding"
:
padding
"padding"
:
string
(
pad_mode
),
"output_size"
:
out_shape
[
1
:
3
]
}
node
.
fluid_code
.
add_layer
(
"conv2d_transpose"
,
inputs
=
input
,
output
=
node
,
param_attr
=
attr
)
if
pad_mode
==
"SAME"
:
if
node
.
tf_data_format
==
"NHWC"
:
out_shape
=
[
out_shape
[
i
]
for
i
in
[
0
,
3
,
1
,
2
]]
for
i
in
range
(
4
):
if
out_shape
[
i
]
<
0
:
out_shape
[
i
]
=
999999
attr
=
{
"axes"
:
[
0
,
1
,
2
,
3
],
"starts"
:
[
0
,
0
,
0
,
0
],
"ends"
:
out_shape
}
node
.
fluid_code
.
add_layer
(
"slice"
,
inputs
=
node
,
output
=
node
,
param_attr
=
attr
)
def
Max
(
self
,
node
):
input
=
self
.
graph
.
get_node
(
node
.
layer
.
input
[
0
],
copy
=
True
)
reduce_idx
=
self
.
graph
.
get_node
(
node
.
layer
.
input
[
1
],
copy
=
True
)
...
...
x2paddle/op_mapper/tf_op_mapper_nhwc.py
浏览文件 @
85e63593
...
...
@@ -321,22 +321,11 @@ class TFOpMapperNHWC(OpMapper):
k_size
=
[
k_size
[
i
]
for
i
in
[
0
,
3
,
1
,
2
]]
input
=
node
if
pad_mode
==
"SAME"
:
pad_h
=
get_same_padding
(
in_shape
[
2
],
k_size
[
2
],
strides
[
2
])
pad_w
=
get_same_padding
(
in_shape
[
3
],
k_size
[
3
],
strides
[
3
])
pad_h
=
pad_h
[
0
]
+
pad_h
[
1
]
pad_w
=
pad_w
[
0
]
+
pad_w
[
1
]
attr
=
{
"paddings"
:
[
0
,
pad_h
,
0
,
pad_w
],
"pad_value"
:
-
10000.0
}
if
pad_h
+
pad_w
!=
0
:
node
.
fluid_code
.
add_layer
(
"pad2d"
,
inputs
=
input
,
output
=
node
,
param_attr
=
attr
)
input
=
node
attr
=
{
"pool_size"
:
k_size
[
2
:
4
],
"pool_type"
:
string
(
"max"
),
"pool_stride"
:
strides
[
2
:
4
]
"pool_stride"
:
strides
[
2
:
4
],
"pool_padding"
:
string
(
pad_mode
)
}
node
.
fluid_code
.
add_layer
(
"pool2d"
,
inputs
=
input
,
...
...
@@ -368,7 +357,6 @@ class TFOpMapperNHWC(OpMapper):
data_format
=
node
.
get_attr
(
"data_format"
).
decode
()
pad_mode
=
node
.
get_attr
(
"padding"
).
decode
()
channel_first
=
data_format
==
"NCHW"
padding
=
0
self
.
weights
[
kernel
.
layer_name
.
replace
(
'/'
,
'_'
)]
=
numpy
.
transpose
(
kernel
.
value
,
(
3
,
2
,
0
,
1
))
...
...
@@ -384,18 +372,6 @@ class TFOpMapperNHWC(OpMapper):
param_attr
=
attr
)
input
=
node
if
pad_mode
==
"SAME"
:
pad_h
=
get_same_padding
(
in_shape
[
2
],
k_size
[
0
],
strides
[
2
])
pad_w
=
get_same_padding
(
in_shape
[
3
],
k_size
[
1
],
strides
[
3
])
if
pad_h
[
0
]
==
pad_h
[
1
]
and
pad_w
[
0
]
==
pad_w
[
1
]:
padding
=
[
pad_h
[
0
],
pad_w
[
0
]]
else
:
attr
=
{
"paddings"
:
pad_h
+
pad_w
,
"pad_value"
:
0.0
}
node
.
fluid_code
.
add_layer
(
"pad2d"
,
inputs
=
input
,
output
=
node
,
param_attr
=
attr
)
input
=
node
attr
=
{
"bias_attr"
:
False
,
"param_attr"
:
string
(
kernel
.
layer_name
),
...
...
@@ -403,7 +379,7 @@ class TFOpMapperNHWC(OpMapper):
"filter_size"
:
k_size
[
0
:
2
],
"stride"
:
strides
[
2
:
4
],
"dilation"
:
dilations
[
2
:
4
],
"padding"
:
padding
"padding"
:
string
(
pad_mode
)
}
node
.
fluid_code
.
add_layer
(
"conv2d"
,
inputs
=
input
,
...
...
@@ -490,7 +466,6 @@ class TFOpMapperNHWC(OpMapper):
data_format
=
node
.
get_attr
(
"data_format"
).
decode
()
pad_mode
=
node
.
get_attr
(
"padding"
).
decode
()
channel_first
=
data_format
==
"NCHW"
padding
=
0
self
.
weights
[
kernel
.
layer_name
.
replace
(
'/'
,
'_'
)]
=
numpy
.
transpose
(
kernel
.
value
,
(
2
,
3
,
0
,
1
))
...
...
@@ -506,19 +481,6 @@ class TFOpMapperNHWC(OpMapper):
param_attr
=
attr
)
input
=
node
if
pad_mode
==
"SAME"
:
pad_h
=
get_same_padding
(
in_shape
[
2
],
k_size
[
0
],
strides
[
2
])
pad_w
=
get_same_padding
(
in_shape
[
3
],
k_size
[
1
],
strides
[
3
])
if
pad_h
[
0
]
==
pad_h
[
1
]
and
pad_w
[
0
]
==
pad_w
[
1
]:
padding
=
[
pad_h
[
0
],
pad_w
[
0
]]
else
:
attr
=
{
"paddings"
:
pad_h
+
pad_w
,
"pad_value"
:
0.0
}
node
.
fluid_code
.
add_layer
(
"pad2d"
,
inputs
=
input
,
output
=
node
,
param_attr
=
attr
)
input
=
node
attr
=
{
"bias_attr"
:
False
,
"param_attr"
:
string
(
kernel
.
layer_name
),
...
...
@@ -528,7 +490,7 @@ class TFOpMapperNHWC(OpMapper):
"dilation"
:
dilations
[
2
:
4
],
"groups"
:
k_size
[
3
]
*
in_shape
[
1
],
"use_cudnn"
:
False
,
"padding"
:
padding
"padding"
:
string
(
pad_mode
)
}
node
.
fluid_code
.
add_layer
(
"conv2d"
,
inputs
=
input
,
...
...
@@ -623,14 +585,9 @@ class TFOpMapperNHWC(OpMapper):
attr
=
{
"pool_size"
:
k_size
[
2
:
4
],
"pool_type"
:
string
(
"avg"
),
"pool_stride"
:
strides
[
2
:
4
]
"pool_stride"
:
strides
[
2
:
4
],
"pool_padding"
:
string
(
pad_mode
)
}
if
pad_mode
==
"SAME"
:
pad_h
=
get_same_padding
(
in_shape
[
2
],
k_size
[
2
],
strides
[
2
])
pad_w
=
get_same_padding
(
in_shape
[
3
],
k_size
[
3
],
strides
[
3
])
assert
pad_h
[
0
]
==
pad_h
[
1
]
and
pad_w
[
0
]
==
pad_w
[
1
],
"Cannot map AvgPool"
attr
[
"pool_padding"
]
=
[
pad_h
[
0
],
pad_w
[
0
]]
node
.
fluid_code
.
add_layer
(
"pool2d"
,
inputs
=
input
,
output
=
node
,
...
...
@@ -990,20 +947,6 @@ class TFOpMapperNHWC(OpMapper):
else
:
self
.
data_format_propagation
(
node
)
padding
=
0
if
pad_mode
==
"SAME"
:
pad_h
=
get_same_padding
(
in_shape
[
2
],
k_size
[
0
],
strides
[
2
])
pad_w
=
get_same_padding
(
in_shape
[
3
],
k_size
[
1
],
strides
[
3
])
if
pad_h
[
0
]
==
pad_h
[
1
]
and
pad_w
[
0
]
==
pad_w
[
1
]:
padding
=
[
pad_h
[
0
],
pad_w
[
0
]]
else
:
attr
=
{
"paddings"
:
pad_h
+
pad_w
,
"pad_value"
:
0.0
}
node
.
fluid_code
.
add_layer
(
"pad2d"
,
inputs
=
input
,
output
=
node
,
param_attr
=
attr
)
input
=
node
attr
=
{
"bias_attr"
:
False
,
"param_attr"
:
string
(
kernel
.
layer_name
),
...
...
@@ -1011,29 +954,14 @@ class TFOpMapperNHWC(OpMapper):
"filter_size"
:
k_size
[
0
:
2
],
"stride"
:
strides
[
2
:
4
],
"dilation"
:
dilations
[
2
:
4
],
"padding"
:
padding
"padding"
:
string
(
pad_mode
),
"output_size"
:
out_shape
[
1
:
3
]
}
node
.
fluid_code
.
add_layer
(
"conv2d_transpose"
,
inputs
=
input
,
output
=
node
,
param_attr
=
attr
)
if
pad_mode
==
"SAME"
:
if
node
.
tf_data_format
==
"NHWC"
:
out_shape
=
[
out_shape
[
i
]
for
i
in
[
0
,
3
,
1
,
2
]]
for
i
in
range
(
4
):
if
out_shape
[
i
]
<
0
:
out_shape
[
i
]
=
999999
attr
=
{
"axes"
:
[
0
,
1
,
2
,
3
],
"starts"
:
[
0
,
0
,
0
,
0
],
"ends"
:
out_shape
}
node
.
fluid_code
.
add_layer
(
"slice"
,
inputs
=
node
,
output
=
node
,
param_attr
=
attr
)
if
not
channel_first
:
attr
=
{
"perm"
:
[
0
,
2
,
3
,
1
]}
node
.
fluid_code
.
add_layer
(
"transpose"
,
...
...
@@ -1181,6 +1109,7 @@ class TFOpMapperNHWC(OpMapper):
else
:
shape
=
self
.
decoder
.
infer_shape_tensor
(
shape
)
attr
=
{
"shape"
:
shape
,
"min"
:
0.0
,
"max"
:
0.9999
}
if
shape
[
0
]
<
0
:
input
=
self
.
batch_node
node
.
fluid_code
.
add_layer
(
"uniform_random_batch_size_like"
,
...
...
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